Multi-Scale Characterization of Pore Structure in Carbonate Formations: Application to the SACROC Unit
- Emmanuel Oyewole (Texas A&M University) | Mehrnoosh Saneifar (Texas A&M University) | Zoya Heidari (Texas A&M University)
- Document ID
- Society of Petrophysicists and Well-Log Analysts
- SPWLA 56th Annual Logging Symposium, 18-22 July, Long Beach, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2015. held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors
- 2 in the last 30 days
- 349 since 2007
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Carbonate formations consist of a wide range of pore types with different shapes, pore-throat sizes, and varying levels of pore-network connectivity. Such heterogeneous pore-network properties affect the fluid flow in the formation. Characterizing pore-network properties (e.g., effective porosity and permeability) in carbonate formations is, however, challenging due to the heterogeneity at different scales and complex pore structure of carbonate rocks. In this paper, we present an integrated technique for the multi-scale characterization of carbonate pore structure based on Mercury Injection Capillary Pressure (MICP) measurements, X-ray microcomputed (micro-CT) three-dimensional (3D) rock images, and well logs. The objectives of this work include (a) characterization of pore structure and poresize distribution using MICP measurements, (b) incorporation of the impact of pore structure, derived from 3D micro-CT core images, on electrical resistivity measurements for reliable assessment of effective porosity and permeability, and (c) petrophysical rock classification based on depth-by-depth estimates of petrophysical properties and mineralogy.
First, we determine the pore types based on the porethroat radius distributions obtained from MICP measurements. The identified pore types are populated in the core domain using the available measurements of porosity and permeability. We introduce a new method for assessment of effective porosity and permeability in the well-log domain, based on pore-scale numerical simulations of fluid and electrical current flows in 3D micro-CT core images, obtained in each pore type. We populate the identified pore types in the well-log domain using fluid-corrected electrical resistivity measurements and our estimates of porosity and permeability. Finally, we conduct petrophysical rock classification based on the depth-by-depth estimates of effective porosity, permeability, volumetric concentrations of minerals, and pore types using an unsupervised artificial neural network.
We successfully applied the proposed technique to three wells in the SACROC (Scurry Area Canyon Reef Operators Committee) Unit. Our results showed that electrical resistivity measurements can be used for reliable characterization of pore structure and assessment of effective porosity and permeability in carbonate formations. The estimates of permeability in the well-log domain were cross-validated using the available core measurements. We observed a 27% improvement in the estimates of permeability, as compared to the core-based porosity-permeability models.
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